As a testbed for our approach, we consider argumentative sentence detection, a sub-task of argument mining. The goal of argument mining is to automatically extract arguments for text collections. One of the major tasks is to identify argument components, such as claims or premises, within a sentence. The problem is formulated as a binary classification task where a sentence can either by labelled as a particular argument component or be evaluated as non-argumentative [1]}. Depending on the annotation schema used in a given corpus, the task is slightly different, as it involves the identification of claims, premises, or both.
Several approaches have tried to shed light on the key properties of arguments, in order to determine common patterns, e.g., syntactic and rhetoric, among single or multiple domains [1]}, [3]}. Primarily inspired by the work by Lippi and Torroni [4]}, we seek to extract tree-structured word patterns that in combination with automatically inferred semantic features may help in the identification of argumentative phrases. To ensure reproducibility, we made all our code and data available at the following repository: https://bitbucket.org/hl2exe/tcgnn/.
